A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data

Enhancing Single-Cell RNA-seq Data Completeness with a Graph Learning Framework

Single cell RNA sequencing (scRNA-seq) is a powerful tool to capture gene expression snapshots in individual cells. However, a low amount of RNA in the individual cells results in dropout events, which introduce huge zero counts in the single cell expression matrix. We have developed VAImpute, a variational graph autoencoder based imputation technique that learns the inherent distribution of a large network/graph constructed from the scRNA-seq data leveraging copula correlation () among cells/genes. The trained model is utilized to predict the dropouts events by computing the probability of all non-edges (cell-gene) in the network. We devise an algorithm to impute the missing expression values of the detected dropouts. The performance of the proposed model is assessed on both simulated and real scRNA-seq datasets, comparing it to established single-cell imputation methods. VAImpute yields significant improvements to detect dropouts, thereby achieving superior performance in cell clustering, detecting rare cells, and differential expression. All codes and datasets are given in the github link: https://github.com/sumantaray/VAImputeAvailability.

Computational frameworks integrating deep learning and statistical models in mining multimodal omics data

BACKGROUND

In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing and differential expression are commonly used in omics analysis. Deep learning, on the other hand, is an advanced computer science technique that is powerful in mining high-dimensional omics data for prediction tasks. Recently, integrative frameworks or methods have been developed for omics studies that combine statistical models and deep learning algorithms.

METHODS AND RESULTS

The aim of these integrative frameworks is to combine the strengths of both statistical methods and deep learning algorithms to improve prediction accuracy while also providing interpretability and explainability. This review report discusses the current state-of-the-art integrative frameworks, their limitations, and potential future directions in survival and time-to-event longitudinal analysis, dimension reduction and clustering, regression and classification, feature selection, and causal and transfer learning.

STGIC: A graph and image convolution-based method for spatial transcriptomic clustering

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

JLONMFSC: Clustering scRNA-seq data based on joint learning of non-negative matrix factorization and subspace clustering

The development of single cell RNA sequencing (scRNA-seq) has provided new perspectives to study biological problems at the single cell level. One of the key issues in scRNA-seq data analysis is to divide cells into several clusters for discovering the heterogeneity and diversity of cells. However, the existing scRNA-seq data are high-dimensional, sparse, and noisy, which challenges the existing single-cell clustering methods. In this study, we propose a joint learning framework (JLONMFSC) for clustering scRNA-seq data. In our method, the dimension of the original data is reduced to minimize the effect of noise. In addition, the graph regularized matrix factorization is used to learn the local features. Further, the Low-Rank Representation (LRR) subspace clustering is utilized to learn the global features. Finally, the joint learning of local features and global features is performed to obtain the results of clustering. We compare the proposed algorithm with eight state-of-the-art algorithms for clustering performance on six datasets, and the experimental results demonstrate that the JLONMFSC achieves better performance in all datasets. The code is avalable at https://github.com/lanbiolab/JLONMFSC.

Single-cell sequencing reveals the landscape of the tumor microenvironment in a skeletal undifferentiated pleomorphic sarcoma patient

Skeletal undifferentiated pleomorphic sarcoma (SUPS) is an invasive pleomorphic soft tissue sarcoma with a high degree of malignancy and poor prognosis. It is prone to recur and metastasize. The tumor microenvironment (TME) and the pathophysiology of SUPS are barely described. Single-cell RNA sequencing (scRNA-seq) provides an opportunity to dissect the landscape of human diseases at an unprecedented resolution, particularly in diseases lacking animal models, such as SUPS. We performed scRNA-seq to analyze tumor tissues and paracancer tissues from a SUPS patient. We identified the cell types and the corresponding marker genes in this SUPS case. We further showed that CD8+ exhausted T cells and Tregs highly expressed PDCD1, CTLA4 and TIGIT. Thus, PDCD1, CTLA4 and TIGIT were identified as potential targets in this case. We applied copy number karyotyping of aneuploid tumors (CopyKAT) to distinguish malignant cells from normal cells in fibroblasts. Our study identified eight malignant fibroblast subsets in SUPS with distinct gene expression profiles. C1-malignant Fibroblast and C6-malignant Fibroblast in the TME play crucial roles in tumor growth, angiogenesis, metastasis and immune response. Hence, targeting malignant fibroblasts could represent a potential strategy for this SUPS therapy. Intervention via tirelizumab enabled disease control, and immune checkpoint inhibitors (ICIs) of PD-1 may be considered as the first-line option in patients with SUPS. Taken together, scRNA-seq analyses provided a powerful basis for this SUPS treatment, improved our understanding of complex human diseases, and may afforded an alternative approach for personalized medicine in the future.